def test():
Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest = utils.readMoonFile()
h = 200
wd = 0.0001
K = 200
maxIter = 500
# neuralNetwork = NeuralNetwork(len(Xtrain[0]), h, utils.getClassCount(ytrain), K=10, wd=wd)
neuralNetworkEfficient = NeuralNetworkEfficient(
len(Xtrain[0]), h, utils.getClassCount(ytrain), K, wd=wd)
# neuralNetworkEfficient._w1 = neuralNetwork._w1
# neuralNetworkEfficient._w2 = neuralNetwork._w2
# neuralNetwork.train(Xtrain, ytrain, maxIter)
# predTrain = neuralNetwork.computePredictions(Xtrain)
# predValid = neuralNetwork.computePredictions(Xvalid)
# predTest = neuralNetwork.computePredictions(Xtest)
# trainEfficiency = utils.calculatePredictionsEfficiency(predTrain, ytrain)
# validEfficiency = utils.calculatePredictionsEfficiency(predValid, yvalid)
# testEfficiency = utils.calculatePredictionsEfficiency(predTest, ytest)
# print( "Train Err: " + "{:.2f}".format(100 - trainEfficiency) + "%" \
# + " / Valid Err: " + "{:.2f}".format(100 - validEfficiency) + "%" \
# + " / Test Err: " + "{:.2f}".format(100 - testEfficiency) + "%")
neuralNetworkEfficient.train(Xtrain, ytrain, maxIter)
predTrain = neuralNetworkEfficient.computePredictions(Xtrain)
predValid = neuralNetworkEfficient.computePredictions(Xvalid)
predTest = neuralNetworkEfficient.computePredictions(Xtest)
trainEfficiency = utils.calculatePredictionsEfficiency(predTrain, ytrain)
validEfficiency = utils.calculatePredictionsEfficiency(predValid, yvalid)
testEfficiency = utils.calculatePredictionsEfficiency(predTest, ytest)
print( "Train Err: " + "{:.2f}".format(100 - trainEfficiency) + "%" \
+ " / Valid Err: " + "{:.2f}".format(100 - validEfficiency) + "%" \
+ " / Test Err: " + "{:.2f}".format(100 - testEfficiency) + "%")
def exo1234():
print("\n\n>>EXERCICE 1 et 2")
sigma = 1e-4
neuralNetwork = NeuralNetwork(2, 2, 2)
Xtrain = [0.7, 0.7]
ytrain = 1 # imaginons que c'est un point de la classe
print("Liste des ratio W1, b1, W2, b2")
res = verifGradient1d(neuralNetwork, Xtrain, ytrain)
print(res)
print(">Tout les ratio sont bien entre 0.99 et 1.01" if False not in
[0.99 < i < 1.01
for i in (np.array(res)).flatten()] else "Echec de la verif..")
print("\n\n>>EXERCICE 3 et 4")
Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest = utils.readMoonFile()
neuralNetwork = NeuralNetwork(len(Xtrain[0]), 5,
utils.getClassCount(ytrain))
K = 10
X = Xtrain[9]
y = ytrain[9]
print("Liste des ratio W1, b1, W2, b2")
res = verifGradient1d(neuralNetwork, X, y)
print(res)
print(">Tout les ratio sont bien entre 0.99 et 1.01" if False not in
[0.99 < i < 1.01
for i in (np.array(res)).flatten()] else "Echec de la verif..")
def test():
Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest = utils.readMoonFile()
h = 200
wd = 0.0001
K = 200
maxIter = 500
# neuralNetwork = NeuralNetwork(len(Xtrain[0]), h, utils.getClassCount(ytrain), K=10, wd=wd)
neuralNetworkEfficient = NeuralNetworkEfficient(len(Xtrain[0]), h, utils.getClassCount(ytrain), K, wd=wd)
# neuralNetworkEfficient._w1 = neuralNetwork._w1
# neuralNetworkEfficient._w2 = neuralNetwork._w2
# neuralNetwork.train(Xtrain, ytrain, maxIter)
# predTrain = neuralNetwork.computePredictions(Xtrain)
# predValid = neuralNetwork.computePredictions(Xvalid)
# predTest = neuralNetwork.computePredictions(Xtest)
# trainEfficiency = utils.calculatePredictionsEfficiency(predTrain, ytrain)
# validEfficiency = utils.calculatePredictionsEfficiency(predValid, yvalid)
# testEfficiency = utils.calculatePredictionsEfficiency(predTest, ytest)
# print( "Train Err: " + "{:.2f}".format(100 - trainEfficiency) + "%" \
# + " / Valid Err: " + "{:.2f}".format(100 - validEfficiency) + "%" \
# + " / Test Err: " + "{:.2f}".format(100 - testEfficiency) + "%")
neuralNetworkEfficient.train(Xtrain, ytrain, maxIter)
predTrain = neuralNetworkEfficient.computePredictions(Xtrain)
predValid = neuralNetworkEfficient.computePredictions(Xvalid)
predTest = neuralNetworkEfficient.computePredictions(Xtest)
trainEfficiency = utils.calculatePredictionsEfficiency(predTrain, ytrain)
validEfficiency = utils.calculatePredictionsEfficiency(predValid, yvalid)
testEfficiency = utils.calculatePredictionsEfficiency(predTest, ytest)
print( "Train Err: " + "{:.2f}".format(100 - trainEfficiency) + "%" \
+ " / Valid Err: " + "{:.2f}".format(100 - validEfficiency) + "%" \
+ " / Test Err: " + "{:.2f}".format(100 - testEfficiency) + "%")
def exo5():
print("\n\n>>EXERCICE 5 Entrainement du reseau de neuronne + Variation des hyper-parametres")
Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest = utils.readMoonFile()
sample_h = [2, 20, 100]
sample_wd = [0, 0.0001, 0.01]
sample_maxIter = [2, 50, 200, 500]
for h in sample_h:
for wd in sample_wd:
for maxIter in sample_maxIter:
trainAndPrint(Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest, h, wd, maxIter)
def exo5():
print(
"\n\n>>EXERCICE 5 Entrainement du reseau de neuronne + Variation des hyper-parametres"
)
Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest = utils.readMoonFile()
sample_h = [2, 20, 100]
sample_wd = [0, 0.0001, 0.01]
sample_maxIter = [2, 50, 200, 500]
for h in sample_h:
for wd in sample_wd:
for maxIter in sample_maxIter:
trainAndPrint(Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest, h,
wd, maxIter)
def exo1234():
print("\n\n>>EXERCICE 1 et 2")
sigma = 1e-4
neuralNetwork = NeuralNetwork(2, 2, 2)
Xtrain = [0.7, 0.7]
ytrain = 1 # imaginons que c'est un point de la classe
print("Liste des ratio W1, b1, W2, b2")
res = verifGradient1d(neuralNetwork, Xtrain, ytrain)
print(res)
print(">Tout les ratio sont bien entre 0.99 et 1.01" if False not in [0.99 < i < 1.01 for i in (
np.array(res)).flatten()] else "Echec de la verif..")
print("\n\n>>EXERCICE 3 et 4")
Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest = utils.readMoonFile()
neuralNetwork = NeuralNetwork(len(Xtrain[0]), 5, utils.getClassCount(ytrain))
K = 10
X = Xtrain[9]
y = ytrain[9]
print("Liste des ratio W1, b1, W2, b2")
res = verifGradient1d(neuralNetwork, X, y)
print(res)
print(">Tout les ratio sont bien entre 0.99 et 1.01" if False not in [0.99 < i < 1.01 for i in (
np.array(res)).flatten()] else "Echec de la verif..")
